df <- read.csv("merge-new-version.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]
df
df$ln_novelty <- log(df$novelty+1)
df$ln_total <- log(df$total+1)
df$group = factor(df$group)
df
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_total ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-4.7373 -0.2178 0.3298 0.8334 1.7253
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.1441 0.1169 44.016 < 2e-16 ***
factor(group)0 -1.0003 0.1642 -6.093 1.94e-09 ***
factor(group)1 -0.4069 0.1612 -2.524 0.011849 *
factor(group)2 -0.5990 0.1603 -3.737 0.000203 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.422 on 628 degrees of freedom
Multiple R-squared: 0.05796, Adjusted R-squared: 0.05346
F-statistic: 12.88 on 3 and 628 DF, p-value: 3.561e-08
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_novelty ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-0.52892 -0.13345 0.06826 0.15783 0.28789
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.52892 0.01759 30.074 < 2e-16 ***
factor(group)0 -0.12226 0.02471 -4.948 9.64e-07 ***
factor(group)1 -0.12367 0.02426 -5.098 4.55e-07 ***
factor(group)2 -0.05178 0.02412 -2.147 0.0322 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.214 on 628 degrees of freedom
Multiple R-squared: 0.05431, Adjusted R-squared: 0.04979
F-statistic: 12.02 on 3 and 628 DF, p-value: 1.163e-07
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod)
Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.73108 -0.10789 0.05269 0.14730 0.30517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.412100 0.035171 11.717 < 2e-16 ***
factor(group)0 -0.113961 0.024192 -4.711 3.06e-06 ***
factor(group)1 -0.116408 0.023889 -4.873 1.40e-06 ***
factor(group)2 -0.051286 0.023555 -2.177 0.02984 *
Q7_Q7_1 -0.020611 0.006956 -2.963 0.00316 **
Q7_Q7_2 0.028904 0.007075 4.085 4.99e-05 ***
Q8_Q8_1 0.008860 0.007319 1.210 0.22656
Q10 0.007122 0.010748 0.663 0.50783
count 0.013293 0.002829 4.699 3.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2067 on 611 degrees of freedom
(12 observations deleted due to missingness)
Multiple R-squared: 0.1234, Adjusted R-squared: 0.112
F-statistic: 10.75 on 8 and 611 DF, p-value: 3.249e-14
df$group <- relevel(df$group, ref = "3")
mod1 <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod1)
Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.73108 -0.10789 0.05269 0.14730 0.30517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.412100 0.035171 11.717 < 2e-16 ***
factor(group)0 -0.113961 0.024192 -4.711 3.06e-06 ***
factor(group)1 -0.116408 0.023889 -4.873 1.40e-06 ***
factor(group)2 -0.051286 0.023555 -2.177 0.02984 *
Q7_Q7_1 -0.020611 0.006956 -2.963 0.00316 **
Q7_Q7_2 0.028904 0.007075 4.085 4.99e-05 ***
Q8_Q8_1 0.008860 0.007319 1.210 0.22656
Q10 0.007122 0.010748 0.663 0.50783
count 0.013293 0.002829 4.699 3.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2067 on 611 degrees of freedom
(12 observations deleted due to missingness)
Multiple R-squared: 0.1234, Adjusted R-squared: 0.112
F-statistic: 10.75 on 8 and 611 DF, p-value: 3.249e-14
anova(mod, mod1)
Analysis of Variance Table
Model 1: ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count
Model 2: ln_novelty ~ factor(group) + factor(phase) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count
Res.Df RSS Df Sum of Sq F Pr(>F)
1 611 26.099
2 608 25.916 3 0.18332 1.4336 0.2319
library(lmerTest)
fit.lmer <- lmer(ln_novelty ~ factor(group) + ( 1 | phase), data = df, REML= FALSE)
fit.lmer
Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
Formula: ln_novelty ~ factor(group) + (1 | phase)
Data: df
AIC BIC logLik deviance df.resid
-147.5364 -120.8431 79.7682 -159.5364 626
Random effects:
Groups Name Std.Dev.
phase (Intercept) 0.005858
Residual 0.213203
Number of obs: 632, groups: phase, 4
Fixed Effects:
(Intercept) factor(group)0 factor(group)1 factor(group)2
0.52892 -0.12226 -0.12367 -0.05178
tapply(df$ln_novelty, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.4842 0.5588 0.5289 0.6162 0.6894
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.5235 0.4067 0.6084 0.6858
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.1777 0.5062 0.4053 0.6182 0.6931
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.3871 0.5465 0.4771 0.6084 0.6904
tapply(df$ln_total, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.331 4.761 5.079 5.144 5.515 5.891
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.106 4.836 4.144 5.337 5.869
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.553 5.089 4.737 5.580 5.882
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.615 4.925 4.545 5.450 5.884
library(vtree)
vtree(df, "group")
vtree(df, c("phase", "group"),
fillcolor = c( phase = "#e7d4e8", group = "#99d8c9"),
horiz = FALSE)
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod)
Call:
lm(formula = ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 +
Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-4.6309 -0.2310 0.3346 0.7764 1.9667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.82832 0.22926 21.060 < 2e-16 ***
factor(group)0 -0.98353 0.15769 -6.237 8.33e-10 ***
factor(group)1 -0.42360 0.15572 -2.720 0.006709 **
factor(group)2 -0.59841 0.15354 -3.897 0.000108 ***
Q7_Q7_1 -0.19585 0.04534 -4.319 1.83e-05 ***
Q7_Q7_2 0.19627 0.04612 4.256 2.41e-05 ***
Q8_Q8_1 -0.10504 0.04771 -2.202 0.028060 *
Q10 0.17920 0.07006 2.558 0.010776 *
count 0.12749 0.01844 6.914 1.19e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.347 on 611 degrees of freedom
(12 observations deleted due to missingness)
Multiple R-squared: 0.1768, Adjusted R-squared: 0.166
F-statistic: 16.4 on 8 and 611 DF, p-value: < 2.2e-16
with(df, interaction.plot(group, phase, ln_total, ylim=c(0, max(ln_total)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot

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